{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from utils import DataGenerator, preprocess_true_boxes\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow.keras.backend as K\n",
    "import tensorflow as tf\n",
    "from pprint import pprint\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "from models import Yolov4, yolov4_head, get_boxes\n",
    "from config import yolo_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../dataset/train_txt2/anno.txt') as f:\n",
    "    lines = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "FOLDER_PATH = '..'\n",
    "anchors = np.array([12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401]).reshape((-1, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dataset/train_img2/test3.jpg 155,1,493,421,0'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_gen = DataGenerator(lines[:1], 1, (416, 416), num_classes=80, folder_path=FOLDER_PATH, anchors=anchors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = data_gen.__getitem__(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 416, 416, 3),\n",
       " 3,\n",
       " [(1, 52, 52, 3, 85), (1, 26, 26, 3, 85), (1, 13, 13, 3, 85)])"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, len(y), [y[i].shape for i in range(3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x146d03290>"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 52, 52, 3, 85)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# y[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# m = 0\n",
    "# for i in range(len(data_gen)):\n",
    "#     X, y = data_gen.__getitem__(i)\n",
    "#     print(y)\n",
    "#     break\n",
    "# #     img = X[0].copy()\n",
    "# #     for b in y[0][0:]:\n",
    "# #         b = [int(i) for i in b]\n",
    "# #         cv2.rectangle(img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)\n",
    "# #     plt.imshow(img)\n",
    "# #     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x146d2c7d0>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw = cv2.imread('../dataset/train_img2/test3.jpg')[:,:,::-1]\n",
    "raw = raw / 255.\n",
    "cv2.rectangle(raw, (155, 1), (493, 421), (0, 0, 255), 2)\n",
    "plt.imshow(raw) # (155, 1), (493, 421) 260 177 491 376"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y[0, :10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def preprocess_true_boxes(true_boxes, input_shape, anchors, num_classes):\n",
    "#     '''Preprocess true boxes to training input format\n",
    "\n",
    "#     Parameters\n",
    "#     ----------\n",
    "#     true_boxes: array, shape=(m, T, 5)\n",
    "#         Absolute x_min, y_min, x_max, y_max, class_id relative to input_shape.\n",
    "#     input_shape: array-like, hw, multiples of 32\n",
    "#     anchors: array, shape=(N, 2), wh\n",
    "#     num_classes: integer\n",
    "\n",
    "#     Returns\n",
    "#     -------\n",
    "#     y_true: list of array, shape like yolo_outputs, xywh are reletive value\n",
    "\n",
    "#     '''\n",
    "#     assert (true_boxes[..., 4]<num_classes).all(), 'class id must be less than num_classes'\n",
    "#     num_layers = len(anchors)//3 # default setting\n",
    "#     print('num_layers ', num_layers)\n",
    "#     anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]\n",
    "\n",
    "#     true_boxes = np.array(true_boxes, dtype='float32')\n",
    "#     input_shape = np.array(input_shape, dtype='int32')\n",
    "#     boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2 #(100, 2) (100, 2)\n",
    "#     boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]\n",
    "#     print('boxes_xy boxes_wh', boxes_xy.shape, boxes_wh.shape)\n",
    "#     true_boxes[..., 0:2] = boxes_xy/input_shape[::-1]\n",
    "#     true_boxes[..., 2:4] = boxes_wh/input_shape[::-1]\n",
    "\n",
    "#     m = true_boxes.shape[0] # bs: 1\n",
    "#     print('m ', m)\n",
    "#     grid_shapes = [input_shape//{0:32, 1:16, 2:8}[l] for l in range(num_layers)]\n",
    "#     y_true = [np.zeros((m,grid_shapes[l][0],grid_shapes[l][1],len(anchor_mask[l]),5+num_classes),\n",
    "#         dtype='float32') for l in range(num_layers)] # (100, 13, 13, 3, 8) (100, 26, 26, 3, 8) (100, 52, 52, 3, 8)\n",
    "#     for i in range(3):\n",
    "#         print(f'y {i}', y_true[i].shape)\n",
    "\n",
    "#     # Expand dim to apply broadcasting.\n",
    "#     anchors = np.expand_dims(anchors, 0) # 1, 9 , 2\n",
    "#     print('anchors ', anchors.shape, anchors) \n",
    "#     anchor_maxes = anchors / 2.\n",
    "#     anchor_mins = -anchor_maxes\n",
    "#     print(anchor_maxes)\n",
    "#     valid_mask = boxes_wh[..., 0]>0  # 100,\n",
    "#     print('valid mask', valid_mask.shape) \n",
    "\n",
    "#     for b in range(m):\n",
    "#         # Discard zero rows.\n",
    "#         wh = boxes_wh[b, valid_mask[b]]  # (1, 2)\n",
    "#         print('wh', wh.shape)\n",
    "#         if len(wh)==0: continue\n",
    "#         # Expand dim to apply broadcasting.\n",
    "#         wh = np.expand_dims(wh, -2) # (1, 1, 2)\n",
    "#         print('wh', wh.shape)\n",
    "# #         print(wh) # 76. 102\n",
    "#         box_maxes = wh / 2. # (1, 1, 2)\n",
    "#         box_mins = -box_maxes # (1, 1, 2)\n",
    "# #         print(box_maxes, box_mins) # [[[38. 51.]]] [[[-38. -51.]]]\n",
    "#         print('box_mins, anchor_mins', box_mins.shape, anchor_mins.shape)\n",
    "#         intersect_mins = np.maximum(box_mins, anchor_mins)\n",
    "#         intersect_maxes = np.minimum(box_maxes, anchor_maxes)\n",
    "#         print('intersect_mins', intersect_mins.shape)\n",
    "# #         print('intersect_mins', 'intersect_max', intersect_mins, intersect_maxes)\n",
    "#         intersect_wh = np.maximum(intersect_maxes - intersect_mins, 0.)\n",
    "# #         print(intersect_wh)\n",
    "#         intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] # (9,)\n",
    "#         print('intersect_area ', intersect_area)\n",
    "# #         print(intersect_area)\n",
    "#         box_area = wh[..., 0] * wh[..., 1]\n",
    "#         print('box_area ', box_area.shape)\n",
    "#         anchor_area = anchors[..., 0] * anchors[..., 1] # 1, 9\n",
    "#         print('anchor_area', anchor_area.shape)\n",
    "#         iou = intersect_area / (box_area + anchor_area - intersect_area)\n",
    "#         print('iou', iou.shape)\n",
    "\n",
    "#         # Find best anchor for each true box\n",
    "#         best_anchor = np.argmax(iou, axis=-1)\n",
    "#         print('best_anchor', best_anchor, best_anchor.shape)\n",
    "#         print('true_boxes ', true_boxes.shape)\n",
    "#         for t, n in enumerate(best_anchor): # t: idx, n:anchor idx\n",
    "#             for l in range(num_layers): # 3\n",
    "#                 if n in anchor_mask[l]:\n",
    "#                     print('true_boxes[b,t,0]', true_boxes[b,t,0], b, t, 'grid_shapes[l]', grid_shapes[l])\n",
    "#                     i = np.floor(true_boxes[b,t,0]*grid_shapes[l][1]).astype('int32') # grid index\n",
    "#                     j = np.floor(true_boxes[b,t,1]*grid_shapes[l][0]).astype('int32') # grid index \n",
    "#                     print(i, j)\n",
    "#                     k = anchor_mask[l].index(n)\n",
    "#                     c = true_boxes[b,t, 4].astype('int32')\n",
    "#                     y_true[l][b, j, i, k, 0:4] = true_boxes[b,t, 0:4] # bbox\n",
    "#                     y_true[l][b, j, i, k, 4] = 1 # confidence\n",
    "#                     y_true[l][b, j, i, k, 5+c] = 1 # one hot\n",
    "\n",
    "#     return y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# a = preprocess_true_boxes(y[:1], (416, 416), anchors, num_classes=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(a)\n",
    "# a[0].shape, a[1].shape, a[2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# yolo_config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/keras/initializers.py:143: calling RandomNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/ops/init_ops.py:97: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    }
   ],
   "source": [
    "model = Yolov4(\n",
    "                weight_path=None,\n",
    "#                 class_name_path='bccd_classes.txt'\n",
    "#               weight_path='yolov4.weights',\n",
    "\n",
    "#                img_size=(416, 416, 3),\n",
    "            \n",
    "              )\n",
    "# model.build_model(load_pretrained=False)\n",
    "model.load_model('test.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "img shape:  (421, 656, 3)\n",
      "# of bboxes: 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x1</th>\n",
       "      <th>y1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y2</th>\n",
       "      <th>class_name</th>\n",
       "      <th>score</th>\n",
       "      <th>w</th>\n",
       "      <th>h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>146</td>\n",
       "      <td>10</td>\n",
       "      <td>509</td>\n",
       "      <td>421</td>\n",
       "      <td>person</td>\n",
       "      <td>0.892524</td>\n",
       "      <td>363</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x1  y1   x2   y2 class_name     score    w    h\n",
       "0  146  10  509  421     person  0.892524  363  411"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('../dataset/train_img2/test3.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# qqqwwwee keras v3 loss\n",
    "# def yolo_loss(y_pred, y_true, anchors, num_classes, ignore_thresh=.5, print_loss=False):\n",
    "#     '''Return yolo_loss tensor\n",
    "\n",
    "#     Parameters\n",
    "#     ----------\n",
    "#     yolo_outputs: list of tensor, the output of yolo_body or tiny_yolo_body\n",
    "#     y_true: list of array, the output of preprocess_true_boxes\n",
    "#     anchors: array, shape=(N, 2), wh\n",
    "#     num_classes: integer\n",
    "#     ignore_thresh: float, the iou threshold whether to ignore object confidence loss\n",
    "\n",
    "#     Returns\n",
    "#     -------\n",
    "#     loss: tensor, shape=(1,)\n",
    "\n",
    "#     '''\n",
    "# #     num_layers = len(anchors)//3 # default setting\n",
    "#     num_layers = 3\n",
    "#     xyscale = [1.2, 1.1, 1.05]\n",
    "# #     yolo_outputs = args[:num_layers]\n",
    "#     yolo_outputs = y_pred\n",
    "# #     y_true = args[num_layers:]\n",
    "# #     y_true \n",
    "# #     anchor_mask = [[6,7,8], [3,4,5], [0,1,2]] if num_layers==3 else [[3,4,5], [1,2,3]]\n",
    "#     anchor_mask = [[0,1,2], [3,4,5], [6,7,8]]\n",
    "#     input_shape = K.cast(K.shape(yolo_outputs[0])[1:3] * 32, K.dtype(y_true[0]))\n",
    "#     grid_shapes = [K.cast(K.shape(yolo_outputs[l])[1:3], K.dtype(y_true[0])) for l in range(num_layers)]\n",
    "#     # (1, 52, 52, 3, 8), (1, 26, 26, 3, 8), (1, 13, 13, 3, 8)\n",
    "#     # -> (52, 52), (26, 26) (13, 13)\n",
    "#     loss = 0\n",
    "#     m = K.shape(yolo_outputs[0])[0] # batch size, tensor\n",
    "#     mf = K.cast(m, tf.float32)\n",
    "# #     mf = K.cast(m, K.dtype(yolo_outputs[0]))\n",
    "\n",
    "#     for l in range(num_layers):\n",
    "#         object_mask = y_true[l][..., 4:5]\n",
    "#         true_class_probs = y_true[l][..., 5:]\n",
    "#         # grid = [[gridx gridy index]]\n",
    "# #         grid, raw_pred, pred_xy, pred_wh = yolo_head(yolo_outputs[l],\n",
    "# #              anchors[anchor_mask[l]], num_classes, input_shape, calc_loss=True)\n",
    "#         bbox0, object_probability0, class_probabilities0, pred_box0 = get_boxes(yolo_outputs[l],\n",
    "#                                                                             anchors=anchors[0, :, :], classes=num_classes,\n",
    "#                                                                             grid_size=52, strides=8,\n",
    "#                                                                             xyscale=xyscale[0])\n",
    "# #         pred_box = K.concatenate([pred_xy, pred_wh])\n",
    "#         pred_box = pred_box0\n",
    "        \n",
    "\n",
    "#         # Darknet raw box to calculate loss.\n",
    "#         # y_true_xy (0~1)\n",
    "#         print('y_true[l][..., :2] ', y_true[l][..., :2], y_true[l][..., :2].shape)\n",
    "#         print('grid_shapes[l][::-1] ', grid_shapes[l][::-1], grid_shapes[l][::-1].shape)\n",
    "# #         print('grid ', grid.shape)\n",
    "#         raw_true_xy = y_true[l][..., :2]*grid_shapes[l][::-1] - grid # raw_true_xy: xy positiion in grid (0~1)\n",
    "#         raw_true_wh = K.log(y_true[l][..., 2:4] / anchors[anchor_mask[l]] * input_shape[::-1])\n",
    "#         raw_true_wh = K.switch(object_mask, raw_true_wh, K.zeros_like(raw_true_wh)) # avoid log(0)=-inf\n",
    "#         box_loss_scale = 2 - y_true[l][...,2:3]*y_true[l][...,3:4]\n",
    "\n",
    "#         # Find ignore mask, iterate over each of batch.\n",
    "#         ignore_mask = tf.TensorArray(K.dtype(y_true[0]), size=1, dynamic_size=True)\n",
    "#         object_mask_bool = K.cast(object_mask, 'bool')\n",
    "#         def loop_body(b, ignore_mask):\n",
    "#             true_box = tf.boolean_mask(y_true[l][b,...,0:4], object_mask_bool[b,...,0])\n",
    "#             iou = box_iou(pred_box[b], true_box)\n",
    "#             best_iou = K.max(iou, axis=-1)\n",
    "#             ignore_mask = ignore_mask.write(b, K.cast(best_iou<ignore_thresh, K.dtype(true_box)))\n",
    "#             return b+1, ignore_mask\n",
    "# #        _, ignore_mask = K.control_flow_ops.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])\n",
    "#         _, ignore_mask = control_flow_ops.while_loop(lambda b,*args: b<m, loop_body, [0, ignore_mask])\n",
    "#         ignore_mask = ignore_mask.stack()\n",
    "#         ignore_mask = K.expand_dims(ignore_mask, -1)\n",
    "\n",
    "#         # K.binary_crossentropy is helpful to avoid exp overflow.\n",
    "#         xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(raw_true_xy, raw_pred[...,0:2], from_logits=True)\n",
    "#         wh_loss = object_mask * box_loss_scale * 0.5 * K.square(raw_true_wh-raw_pred[...,2:4])\n",
    "#         confidence_loss = object_mask * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True)+ \\\n",
    "#             (1-object_mask) * K.binary_crossentropy(object_mask, raw_pred[...,4:5], from_logits=True) * ignore_mask\n",
    "#         class_loss = object_mask * K.binary_crossentropy(true_class_probs, raw_pred[...,5:], from_logits=True)\n",
    "\n",
    "#         xy_loss = K.sum(xy_loss) / mf\n",
    "#         wh_loss = K.sum(wh_loss) / mf\n",
    "#         confidence_loss = K.sum(confidence_loss) / mf\n",
    "#         class_loss = K.sum(class_loss) / mf\n",
    "#         loss += xy_loss + wh_loss + confidence_loss + class_loss\n",
    "#         if print_loss:\n",
    "#             loss = tf.Print(loss, [loss, xy_loss, wh_loss, confidence_loss, class_loss, K.sum(ignore_mask)], message='loss: ')\n",
    "#     return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def broadcast_iou(box_1, box_2):\n",
    "#     box_1 = tf.expand_dims(box_1, -2)\n",
    "#     box_2 = tf.expand_dims(box_2, 0)\n",
    "#     new_shape = tf.broadcast_dynamic_shape(tf.shape(box_1), tf.shape(box_2))\n",
    "#     box_1 = tf.broadcast_to(box_1, new_shape)\n",
    "#     box_2 = tf.broadcast_to(box_2, new_shape)\n",
    "#     int_w = tf.maximum(\n",
    "#         tf.minimum(box_1[..., 2], box_2[..., 2])\n",
    "#         - tf.maximum(box_1[..., 0], box_2[..., 0]),\n",
    "#         0,\n",
    "#     )\n",
    "#     int_h = tf.maximum(\n",
    "#         tf.minimum(box_1[..., 3], box_2[..., 3])\n",
    "#         - tf.maximum(box_1[..., 1], box_2[..., 1]),\n",
    "#         0,\n",
    "#     )\n",
    "#     int_area = int_w * int_h\n",
    "#     box_1_area = (box_1[..., 2] - box_1[..., 0]) * (\n",
    "#         box_1[..., 3] - box_1[..., 1]\n",
    "#     )\n",
    "#     box_2_area = (box_2[..., 2] - box_2[..., 0]) * (\n",
    "#         box_2[..., 3] - box_2[..., 1]\n",
    "#     )\n",
    "#     return int_area / (box_1_area + box_2_area - int_area)\n",
    "\n",
    "# def yolo_loss(y_true, y_pred, num_classes, anchors, xyscale=[1.2, 1.1, 1.05], ignore_thresh=0.5):\n",
    "# #     get_boxes(yolo_neck_outputs[0],\n",
    "# #             anchors=anchors[0, :, :], classes=80,\n",
    "# #             grid_size=52, strides=8,\n",
    "# #             xyscale=xyscale[0])\n",
    "#     yolo_outputs = tf.cast(y_pred[0], tf.float32)\n",
    "#     y_true = y_true[0]\n",
    "#     anchors = anchors[0]\n",
    "#     print('yolo_outputs ', yolo_outputs.shape, 'y_true', y_true.shape)\n",
    "#     pred_box_x1y1x2y2, object_probability0, class_probabilities0, pred_box_wywh = get_boxes(yolo_outputs,\n",
    "#                                                                     anchors=anchors, classes=num_classes,\n",
    "#                                                                     grid_size=52, strides=8,\n",
    "#                                                                     xyscale=xyscale[0])\n",
    "# #     pred_box, pred_obj, pred_class, pred_xywh = get_boxes(\n",
    "# #         y_pred, anchors, classes\n",
    "# #     )\n",
    "#     pred_box = pred_box_x1y1x2y2\n",
    "#     pred_obj = object_probability0\n",
    "#     pred_class = class_probabilities0\n",
    "#     pred_xywh = pred_box_xywh\n",
    "#     pred_xy = pred_xywh[..., 0:2]\n",
    "#     pred_wh = pred_xywh[..., 2:4]\n",
    "# #     true_box, true_obj, true_class_idx = tf.split(\n",
    "# #         y_true, (4, 1, 1), axis=-1\n",
    "# #     )\n",
    "#     true_box = y_true[..., :4]\n",
    "#     true_obj = y_true[..., 4]\n",
    "#     true_classes = y_true[..., 5:]\n",
    "#     print('true_classes ', true_classes.shape)\n",
    "# #     true_class_idx\n",
    "# #     true_xy = (true_box[..., 0:2] + true_box[..., 2:4]) / 2\n",
    "# #     true_wh = true_box[..., 2:4] - true_box[..., 0:2]\n",
    "#     true_xy = true_box[..., :2]\n",
    "#     true_wh = true_box[..., 2:4]\n",
    "    \n",
    "#     box_loss_scale = 2 - true_wh[..., 0] * true_wh[..., 1]\n",
    "#     grid_size = tf.shape(y_true)[1]\n",
    "#     grid = tf.meshgrid(tf.range(grid_size), tf.range(grid_size))\n",
    "#     grid = tf.expand_dims(tf.stack(grid, axis=-1), axis=2)\n",
    "#     true_xy = true_xy * tf.cast(grid_size, tf.float32) - tf.cast(\n",
    "#         grid, tf.float32\n",
    "#     )\n",
    "# #     true_wh  (1, 52, 52, 3, 2)\n",
    "# #     anchors  (3, 2)\n",
    "#     true_wh = tf.math.log(true_wh / anchors)\n",
    "#     true_wh = tf.where(\n",
    "#         tf.math.is_inf(true_wh), tf.zeros_like(true_wh), true_wh\n",
    "#     )\n",
    "#     true_wh = tf.cast(true_wh, tf.float32)\n",
    "# #     true obj  (1, 52, 52, 3)\n",
    "# #     obj_mask = tf.squeeze(true_obj, -1)\n",
    "#     obj_mask = true_obj\n",
    "#     print('pred_box, true_box, obj_mask', pred_box.shape, true_box.shape, obj_mask.shape)\n",
    "#     # pred_box, true_box, obj_mask (1, 52, 52, 3, 4) (1, 52, 52, 3, 4) (1, 52, 52, 3)\n",
    "#     best_iou = tf.map_fn(\n",
    "#         lambda x: tf.reduce_max(\n",
    "#             broadcast_iou(\n",
    "#                 x[0], tf.boolean_mask(x[1], tf.cast(x[2], tf.bool))\n",
    "#             ),\n",
    "#             axis=-1,\n",
    "#         ),\n",
    "#         (pred_box, true_box, obj_mask),\n",
    "#         tf.float32,\n",
    "#     )\n",
    "#     ignore_mask = tf.cast(best_iou < ignore_thresh, tf.float32)\n",
    "#     xy_loss = (\n",
    "#         obj_mask\n",
    "#         * box_loss_scale\n",
    "#         * tf.reduce_sum(tf.square(true_xy - pred_xy), axis=-1)\n",
    "#     )\n",
    "#     wh_loss = (\n",
    "#         obj_mask\n",
    "#         * box_loss_scale\n",
    "#         * tf.reduce_sum(tf.square(true_wh - pred_wh), axis=-1)\n",
    "#     )\n",
    "#     print('true_obj pred_obj', true_obj.shape, pred_obj.shape)\n",
    "#     # true_obj pred_obj (1, 52, 52, 3) (1, 52, 52, 3, 1)\n",
    "#     obj_loss = tf.keras.losses.binary_crossentropy(tf.expand_dims(true_obj, -1), pred_obj)\n",
    "#     print('obj_mask, obj_loss, ignore_mask', obj_mask.shape, obj_loss.shape, ignore_mask.shape)\n",
    "#     # obj_mask, obj_loss, ignore_mask (1, 52, 52, 3) (1, 52, 52, 3) (1, 52, 52, 3)\n",
    "#     obj_loss = (\n",
    "#         obj_mask * obj_loss + (1 - obj_mask) * ignore_mask * obj_loss\n",
    "#     )\n",
    "#     print('pred_class ', pred_class.shape)\n",
    "#     class_loss = obj_mask * tf.keras.losses.categorical_crossentropy(\n",
    "#         true_classes, pred_class\n",
    "#     )\n",
    "#     xy_loss = tf.reduce_sum(xy_loss, axis=(1, 2, 3))\n",
    "#     wh_loss = tf.reduce_sum(wh_loss, axis=(1, 2, 3))\n",
    "#     obj_loss = tf.reduce_sum(obj_loss, axis=(1, 2, 3))\n",
    "#     class_loss = tf.reduce_sum(class_loss, axis=(1, 2, 3))\n",
    "#     return xy_loss + wh_loss + obj_loss + class_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tflite yolov4\n",
    "def bbox_giou(bboxes1, bboxes2):\n",
    "    \"\"\"\n",
    "    Generalized IoU\n",
    "    @param bboxes1: (a, b, ..., 4)\n",
    "    @param bboxes2: (A, B, ..., 4)\n",
    "        x:X is 1:n or n:n or n:1\n",
    "    @return (max(a,A), max(b,B), ...)\n",
    "    ex) (4,):(3,4) -> (3,)\n",
    "        (2,1,4):(2,3,4) -> (2,3)\n",
    "    \"\"\"\n",
    "    bboxes1_area = bboxes1[..., 2] * bboxes1[..., 3]\n",
    "    bboxes2_area = bboxes2[..., 2] * bboxes2[..., 3]\n",
    "\n",
    "    bboxes1_coor = tf.concat(\n",
    "        [\n",
    "            bboxes1[..., :2] - bboxes1[..., 2:] * 0.5,\n",
    "            bboxes1[..., :2] + bboxes1[..., 2:] * 0.5,\n",
    "        ],\n",
    "        axis=-1,\n",
    "    )\n",
    "    bboxes2_coor = tf.concat(\n",
    "        [\n",
    "            bboxes2[..., :2] - bboxes2[..., 2:] * 0.5,\n",
    "            bboxes2[..., :2] + bboxes2[..., 2:] * 0.5,\n",
    "        ],\n",
    "        axis=-1,\n",
    "    )\n",
    "\n",
    "    left_up = tf.maximum(bboxes1_coor[..., :2], bboxes2_coor[..., :2])\n",
    "    right_down = tf.minimum(bboxes1_coor[..., 2:], bboxes2_coor[..., 2:])\n",
    "\n",
    "    inter_section = tf.maximum(right_down - left_up, 0.0)\n",
    "    inter_area = inter_section[..., 0] * inter_section[..., 1]\n",
    "\n",
    "    union_area = bboxes1_area + bboxes2_area - inter_area\n",
    "\n",
    "    iou = tf.math.divide_no_nan(inter_area, union_area)\n",
    "\n",
    "    enclose_left_up = tf.minimum(bboxes1_coor[..., :2], bboxes2_coor[..., :2])\n",
    "    enclose_right_down = tf.maximum(\n",
    "        bboxes1_coor[..., 2:], bboxes2_coor[..., 2:]\n",
    "    )\n",
    "\n",
    "    enclose_section = enclose_right_down - enclose_left_up\n",
    "    enclose_area = enclose_section[..., 0] * enclose_section[..., 1]\n",
    "\n",
    "    giou = iou - tf.math.divide_no_nan(enclose_area - union_area, enclose_area)\n",
    "\n",
    "    return giou\n",
    "def bbox_iou(bboxes1, bboxes2):\n",
    "    \"\"\"\n",
    "    @param bboxes1: (a, b, ..., 4)\n",
    "    @param bboxes2: (A, B, ..., 4)\n",
    "        x:X is 1:n or n:n or n:1\n",
    "    @return (max(a,A), max(b,B), ...)\n",
    "    ex) (4,):(3,4) -> (3,)\n",
    "        (2,1,4):(2,3,4) -> (2,3)\n",
    "    \"\"\"\n",
    "    bboxes1_area = bboxes1[..., 2] * bboxes1[..., 3]\n",
    "    bboxes2_area = bboxes2[..., 2] * bboxes2[..., 3]\n",
    "\n",
    "    bboxes1_coor = tf.concat(\n",
    "        [\n",
    "            bboxes1[..., :2] - bboxes1[..., 2:] * 0.5,\n",
    "            bboxes1[..., :2] + bboxes1[..., 2:] * 0.5,\n",
    "        ],\n",
    "        axis=-1,\n",
    "    )\n",
    "    bboxes2_coor = tf.concat(\n",
    "        [\n",
    "            bboxes2[..., :2] - bboxes2[..., 2:] * 0.5,\n",
    "            bboxes2[..., :2] + bboxes2[..., 2:] * 0.5,\n",
    "        ],\n",
    "        axis=-1,\n",
    "    )\n",
    "\n",
    "    left_up = tf.maximum(bboxes1_coor[..., :2], bboxes2_coor[..., :2])\n",
    "    right_down = tf.minimum(bboxes1_coor[..., 2:], bboxes2_coor[..., 2:])\n",
    "\n",
    "    inter_section = tf.maximum(right_down - left_up, 0.0)\n",
    "    inter_area = inter_section[..., 0] * inter_section[..., 1]\n",
    "\n",
    "    union_area = bboxes1_area + bboxes2_area - inter_area\n",
    "\n",
    "    iou = tf.math.divide_no_nan(inter_area, union_area)\n",
    "\n",
    "    return iou\n",
    "def yolo_loss_wrapper(input_shape, STRIDES, NUM_CLASS, ANCHORS, XYSCALES, IOU_LOSS_THRESH):\n",
    "    input_shape = input_shape[0]\n",
    "    def yolo_loss(y_true, y_pred):\n",
    "#         num_stages = 3 # len(y_true)\n",
    "#         input_shape = input_shape[0]\n",
    "        # conv_shape = tf.shape(conv)\n",
    "        bboxes_tensor = decode_train2(y_pred, input_shape // STRIDES, NUM_CLASS, STRIDES, ANCHORS, XYSCALES)\n",
    "        raw_convs = y_pred\n",
    "\n",
    "        total_loss = 0\n",
    "#         for stage in range(num_stages):\n",
    "        label = y_true\n",
    "        bboxes = bboxes_tensor\n",
    "        batch_size = tf.shape(label)[0]\n",
    "        grid_size = tf.shape(label)[1]\n",
    "        output_size = grid_size\n",
    "        input_size = STRIDES * output_size\n",
    "        conv = raw_convs\n",
    "        # pred = conv\n",
    "        # pred = tf.sigmoid(pred)\n",
    "\n",
    "        conv = tf.reshape(conv, (batch_size, output_size, output_size, 3, 5 + NUM_CLASS))\n",
    "        # conv_raw_conf = conv[:, :, :, :, 4:5]\n",
    "        # conv_raw_prob = conv[:, :, :, :, 5:]\n",
    "        conv_raw_conf = bboxes[..., 4:5]\n",
    "        conv_raw_prob = bboxes[..., 5:]\n",
    "\n",
    "        pred_xywh = bboxes[:, :, :, :, 0:4]  # abs xy wh\n",
    "        pred_conf = bboxes[:, :, :, :, 4:5]\n",
    "        # pred_xywh = pred[:, :, :, :, 0:4]  # abs xy wh\n",
    "        # pred_conf = pred[:, :, :, :, 4:5]\n",
    "\n",
    "        label_xywh = label[:, :, :, :, 0:4]\n",
    "        respond_bbox = label[:, :, :, :, 4:5]\n",
    "        label_prob = label[:, :, :, :, 5:]\n",
    "\n",
    "        giou = tf.expand_dims(bbox_giou(pred_xywh, label_xywh), axis=-1)\n",
    "        input_size = tf.cast(input_size, tf.float32)\n",
    "\n",
    "        bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2)\n",
    "        giou_loss = respond_bbox * bbox_loss_scale * (1 - giou)\n",
    "\n",
    "        # iou = utils.bbox_iou(pred_xywh[:, :, :, :, np.newaxis, :], bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :])\n",
    "        #\n",
    "        # iou = bbox_iou(pred_xywh, bboxes)\n",
    "        iou = bbox_iou(pred_xywh, label_xywh)\n",
    "\n",
    "        # max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1)\n",
    "        max_iou = tf.reduce_max(iou, axis=-1)[..., tf.newaxis, tf.newaxis]\n",
    "        respond_bgd = (1.0 - respond_bbox) * tf.cast(max_iou < IOU_LOSS_THRESH, tf.float32)\n",
    "\n",
    "        conf_focal = tf.pow(respond_bbox - pred_conf, 2)\n",
    "\n",
    "        conf_loss = conf_focal * (\n",
    "                respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)\n",
    "                +\n",
    "                respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)\n",
    "        )\n",
    "\n",
    "        prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob)\n",
    "\n",
    "        giou_loss = tf.reduce_mean(tf.reduce_sum(giou_loss, axis=[1, 2, 3, 4]))\n",
    "        conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1, 2, 3, 4]))\n",
    "        prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1, 2, 3, 4]))\n",
    "\n",
    "        return giou_loss + conf_loss + prob_loss\n",
    "    return yolo_loss\n",
    "\n",
    "def decode_train2(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, XYSCALE):\n",
    "    conv_output = tf.reshape(conv_output,\n",
    "                             (tf.shape(conv_output)[0], output_size, output_size, 3, 5 + NUM_CLASS))\n",
    "\n",
    "    conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS),\n",
    "                                                                          axis=-1)\n",
    "\n",
    "    xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))\n",
    "    xy_grid = tf.expand_dims(tf.stack(xy_grid, axis=-1), axis=2)  # [gx, gy, 1, 2]\n",
    "    xy_grid = tf.tile(tf.expand_dims(xy_grid, axis=0), [tf.shape(conv_output)[0], 1, 1, 3, 1])\n",
    "\n",
    "    xy_grid = tf.cast(xy_grid, tf.float32)\n",
    "\n",
    "    pred_xy = ((tf.sigmoid(conv_raw_dxdy) * XYSCALE) - 0.5 * (XYSCALE - 1) + xy_grid) * STRIDES\n",
    "    pred_wh = (tf.exp(conv_raw_dwdh) * ANCHORS)\n",
    "    pred_xywh = tf.concat([pred_xy, pred_wh], axis=-1)\n",
    "\n",
    "    pred_conf = tf.sigmoid(conv_raw_conf)\n",
    "    pred_prob = tf.sigmoid(conv_raw_prob)\n",
    "\n",
    "    return tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_batch, y_batch = X, y = data_gen.__getitem__(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1, 416, 416, 3),\n",
       " [(1, 52, 52, 3, 85), (1, 26, 26, 3, 85), (1, 13, 13, 3, 85)])"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_batch.shape, [y_batch[i].shape for i in range(3) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[[ 1.92684323e-01,  4.70553339e-03, -3.25091064e-01, ...,\n",
       "           -7.48140192e+00, -7.33025074e+00, -7.60484362e+00],\n",
       "          [ 3.59633535e-01,  9.13126767e-02,  7.17952967e-01, ...,\n",
       "           -7.92454815e+00, -7.53366756e+00, -8.67181492e+00],\n",
       "          [-4.70207512e-01, -3.26502740e-01,  1.07563639e+00, ...,\n",
       "           -7.31230688e+00, -6.89751053e+00, -8.53446293e+00],\n",
       "          ...,\n",
       "          [-6.78797424e-01, -1.48754969e-01,  1.41953123e+00, ...,\n",
       "           -7.05732584e+00, -5.15669870e+00, -5.36032724e+00],\n",
       "          [ 1.47106245e-01,  1.78823709e-01,  6.92542076e-01, ...,\n",
       "           -6.79594231e+00, -5.60554981e+00, -3.55692625e+00],\n",
       "          [-1.05126508e-01,  2.54495710e-01, -5.65513372e-02, ...,\n",
       "           -5.98874664e+00, -5.99938583e+00, -3.46918869e+00]],\n",
       " \n",
       "         [[-1.14882156e-01,  1.22810304e-01, -5.33798993e-01, ...,\n",
       "           -7.98593187e+00, -7.31593752e+00, -7.97113085e+00],\n",
       "          [ 1.22921050e-01, -2.57019401e-01,  6.71055496e-01, ...,\n",
       "           -8.63301849e+00, -7.66632938e+00, -9.30048466e+00],\n",
       "          [-5.26184618e-01, -8.09042811e-01,  9.42425668e-01, ...,\n",
       "           -8.23746109e+00, -7.32318592e+00, -9.27010441e+00],\n",
       "          ...,\n",
       "          [-3.66958439e-01, -1.27165034e-01,  1.17210770e+00, ...,\n",
       "           -8.19813538e+00, -5.69611597e+00, -5.81780338e+00],\n",
       "          [ 9.09052551e-01,  2.92296827e-01,  8.28130841e-01, ...,\n",
       "           -7.66847467e+00, -6.38649893e+00, -4.10181808e+00],\n",
       "          [ 3.98394078e-01,  1.64160848e-01, -4.66917932e-01, ...,\n",
       "           -6.56330824e+00, -6.19342852e+00, -3.56396627e+00]],\n",
       " \n",
       "         [[-2.33395472e-01, -1.73300430e-01, -5.45994580e-01, ...,\n",
       "           -7.80131340e+00, -7.39047050e+00, -7.72769308e+00],\n",
       "          [-3.31119537e-01, -5.80449760e-01,  5.25855184e-01, ...,\n",
       "           -8.42267036e+00, -7.55218601e+00, -8.66142082e+00],\n",
       "          [-2.45848417e-01, -3.91354084e-01,  6.28797591e-01, ...,\n",
       "           -8.20294666e+00, -6.84757137e+00, -8.66795349e+00],\n",
       "          ...,\n",
       "          [-3.02816510e-01,  3.68183732e-01,  9.90348279e-01, ...,\n",
       "           -7.85714769e+00, -5.47617626e+00, -6.27379560e+00],\n",
       "          [ 1.02110505e+00, -1.18365571e-01,  6.40064538e-01, ...,\n",
       "           -7.13223886e+00, -6.26541042e+00, -5.19165993e+00],\n",
       "          [ 1.00874853e+00,  8.03500265e-02, -8.17086756e-01, ...,\n",
       "           -6.51000500e+00, -6.67859364e+00, -5.30535126e+00]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[-3.72652680e-01,  4.66424450e-02, -5.50477564e-01, ...,\n",
       "           -5.03637791e+00, -5.47866249e+00, -6.29844379e+00],\n",
       "          [-5.72283268e-01,  3.32549393e-01,  6.05177522e-01, ...,\n",
       "           -4.50381851e+00, -5.70353889e+00, -6.19998026e+00],\n",
       "          [-9.93401110e-01,  4.38172400e-01,  7.95917392e-01, ...,\n",
       "           -4.61251545e+00, -5.91228580e+00, -5.76834822e+00],\n",
       "          ...,\n",
       "          [ 3.96062821e-01,  6.55428231e-01,  9.07582045e-01, ...,\n",
       "           -5.38578033e+00, -6.53931713e+00, -5.29411602e+00],\n",
       "          [ 1.24887526e+00,  5.65961838e-01,  5.11207998e-01, ...,\n",
       "           -5.50962591e+00, -6.61188745e+00, -5.52874947e+00],\n",
       "          [ 1.53941944e-01,  7.84965605e-02, -3.64218175e-01, ...,\n",
       "           -5.66650963e+00, -6.55830574e+00, -5.94279385e+00]],\n",
       " \n",
       "         [[-2.47013122e-01,  3.22174728e-02, -4.85768318e-01, ...,\n",
       "           -4.78901100e+00, -5.02545738e+00, -5.84945583e+00],\n",
       "          [-1.45604938e-01,  3.69515494e-02,  6.86837316e-01, ...,\n",
       "           -4.25466585e+00, -5.33411169e+00, -5.91544437e+00],\n",
       "          [-7.25136399e-01,  6.15517795e-01,  1.16937232e+00, ...,\n",
       "           -4.28063536e+00, -5.54206467e+00, -5.58701229e+00],\n",
       "          ...,\n",
       "          [ 2.56230116e-01,  9.57646072e-01,  1.21804547e+00, ...,\n",
       "           -5.44446945e+00, -5.70161057e+00, -4.65797138e+00],\n",
       "          [ 5.32424450e-01,  2.69397646e-01,  6.31769061e-01, ...,\n",
       "           -5.56935692e+00, -5.94148540e+00, -5.10994387e+00],\n",
       "          [ 2.07718924e-01, -8.58640671e-03, -4.25779462e-01, ...,\n",
       "           -5.59699821e+00, -6.06625843e+00, -5.65241528e+00]],\n",
       " \n",
       "         [[-1.43056676e-01,  1.21569075e-01, -4.18126166e-01, ...,\n",
       "           -4.46983242e+00, -4.98891354e+00, -5.85479355e+00],\n",
       "          [-8.39048401e-02,  2.86272377e-01,  6.77199364e-01, ...,\n",
       "           -4.32999516e+00, -5.19570923e+00, -5.75361061e+00],\n",
       "          [-3.30368966e-01,  6.26062393e-01,  1.18753338e+00, ...,\n",
       "           -4.19158840e+00, -5.56228161e+00, -5.62144279e+00],\n",
       "          ...,\n",
       "          [-6.22801557e-02,  8.44494700e-01,  1.32911420e+00, ...,\n",
       "           -5.86677408e+00, -5.92653179e+00, -4.80185032e+00],\n",
       "          [ 2.61066645e-01,  4.48718548e-01,  6.75522983e-01, ...,\n",
       "           -5.57586861e+00, -5.81552553e+00, -5.12986183e+00],\n",
       "          [ 3.38103563e-01,  1.47130340e-02, -4.80338216e-01, ...,\n",
       "           -5.17455816e+00, -5.89644337e+00, -5.58599567e+00]]]],\n",
       "       dtype=float32), (1, 52, 52, 255), (1, 26, 26, 255), (1, 13, 13, 255))"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.yolo_model.predict(x_batch)\n",
    "y_pred[0], y_pred[0].shape, y_pred[1].shape, y_pred[2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.6373584,  2.7128265,  2.8486443], dtype=float32)"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_large = np.reshape(y_pred[2], (1, 13,13,3,85))\n",
    "pred_large[0].shape\n",
    "pred_large[0, 6,6,:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  7.636606 , -11.252513 , -11.441111 , -12.96235  , -11.584581 ,\n",
       "        -10.93054  ,  -9.138035 , -11.347572 , -11.335099 , -12.179684 ,\n",
       "        -13.4807   , -12.16067  , -12.46946  , -11.511032 , -13.44124  ,\n",
       "        -10.28499  , -12.381161 , -12.594769 , -15.289934 , -14.314249 ,\n",
       "        -13.6186695, -13.47594  , -14.472248 , -13.561726 , -10.245218 ,\n",
       "        -10.248945 , -10.158533 , -10.047979 ,  -9.22928  , -13.340868 ,\n",
       "        -11.557777 , -13.176586 , -12.204084 , -13.422211 , -12.226296 ,\n",
       "        -13.403009 , -11.704605 , -11.341715 , -12.506599 , -10.170585 ,\n",
       "        -13.066299 , -12.255553 , -13.978083 , -12.955928 , -13.523987 ,\n",
       "        -12.741533 , -12.994468 , -12.407428 , -13.290152 , -13.36681  ,\n",
       "        -14.730848 , -12.739487 , -13.045448 , -13.122875 , -13.226494 ,\n",
       "        -12.824196 , -11.116887 , -11.433914 , -12.644731 ,  -9.282808 ,\n",
       "         -8.865642 , -12.049185 ,  -9.141024 , -10.2208185, -11.804696 ,\n",
       "        -12.14802  , -11.335205 , -10.0374565, -10.338556 , -10.548193 ,\n",
       "        -11.607027 , -11.391785 ,  -8.348221 ,  -8.529457 , -11.365282 ,\n",
       "        -13.187103 , -10.917531 , -13.677565 , -11.909955 , -11.971456 ],\n",
       "       dtype=float32),\n",
       " array([  7.3692336, -10.784485 , -11.064957 , -12.367368 , -11.218458 ,\n",
       "        -10.327759 ,  -8.683085 , -10.639738 , -11.058219 , -11.627007 ,\n",
       "        -12.866922 , -11.515596 , -11.708408 , -11.365585 , -12.819001 ,\n",
       "         -9.614036 , -11.992219 , -12.142094 , -13.682758 , -13.268255 ,\n",
       "        -12.803651 , -12.361914 , -13.3539295, -12.940077 , -10.444544 ,\n",
       "         -9.822193 , -10.099232 , -10.03968  ,  -8.937082 , -12.569984 ,\n",
       "        -11.020581 , -12.370913 , -11.635272 , -12.773396 , -11.518697 ,\n",
       "        -12.58625  , -11.202156 , -10.798545 , -12.092903 ,  -9.395587 ,\n",
       "        -11.820595 , -11.122238 , -13.046115 , -12.334538 , -13.111229 ,\n",
       "        -12.297735 , -12.218702 , -11.579716 , -12.314984 , -12.085575 ,\n",
       "        -13.084683 , -11.717102 , -12.256525 , -12.363829 , -12.208319 ,\n",
       "        -11.799492 , -10.885243 , -11.290566 , -12.307642 ,  -9.183685 ,\n",
       "         -8.871944 , -11.763248 ,  -8.925151 ,  -9.962894 , -11.4180355,\n",
       "        -11.490107 , -11.027035 ,  -9.967045 , -10.174149 , -10.306366 ,\n",
       "        -11.010529 , -11.195772 ,  -8.2137   ,  -8.531804 , -10.468336 ,\n",
       "        -12.097195 , -10.926149 , -13.19644  , -11.738222 , -11.670326 ],\n",
       "       dtype=float32))"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_large[0, 6,6,1,5:], pred_large[0, 6,6,2,5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8486443"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(pred_large[0, :,:,:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 6, 1, 5)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unravel_index(pred_large[0].argmax(), pred_large[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# pprint(y_batch[2][0, 6, :, :, :4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bbox_giou(bboxes1, bboxes2):\n",
    "    \"\"\"\n",
    "    Generalized IoU\n",
    "    @param bboxes1: (a, b, ..., 4)\n",
    "    @param bboxes2: (A, B, ..., 4)\n",
    "        x:X is 1:n or n:n or n:1\n",
    "    @return (max(a,A), max(b,B), ...)\n",
    "    ex) (4,):(3,4) -> (3,)\n",
    "        (2,1,4):(2,3,4) -> (2,3)\n",
    "    \"\"\"\n",
    "    bboxes1_area = bboxes1[..., 2] * bboxes1[..., 3]\n",
    "    bboxes2_area = bboxes2[..., 2] * bboxes2[..., 3]\n",
    "\n",
    "    bboxes1_coor = np.concatenate(\n",
    "        [\n",
    "            bboxes1[..., :2] - bboxes1[..., 2:] * 0.5,\n",
    "            bboxes1[..., :2] + bboxes1[..., 2:] * 0.5,\n",
    "        ],\n",
    "        axis=-1,\n",
    "    )\n",
    "    bboxes2_coor = np.concatenate(\n",
    "        [\n",
    "            bboxes2[..., :2] - bboxes2[..., 2:] * 0.5,\n",
    "            bboxes2[..., :2] + bboxes2[..., 2:] * 0.5,\n",
    "        ],\n",
    "        axis=-1,\n",
    "    )\n",
    "\n",
    "    left_up = np.maximum(bboxes1_coor[..., :2], bboxes2_coor[..., :2])\n",
    "    right_down = np.minimum(bboxes1_coor[..., 2:], bboxes2_coor[..., 2:])\n",
    "\n",
    "    inter_section = np.maximum(right_down - left_up, 0.0)\n",
    "    inter_area = inter_section[..., 0] * inter_section[..., 1]\n",
    "\n",
    "    union_area = bboxes1_area + bboxes2_area - inter_area\n",
    "\n",
    "    iou = np.nan_to_num(inter_area/union_area)\n",
    "\n",
    "    enclose_left_up = np.minimum(bboxes1_coor[..., :2], bboxes2_coor[..., :2])\n",
    "    enclose_right_down = np.maximum(\n",
    "        bboxes1_coor[..., 2:], bboxes2_coor[..., 2:]\n",
    "    )\n",
    "\n",
    "    enclose_section = enclose_right_down - enclose_left_up\n",
    "    enclose_area = enclose_section[..., 0] * enclose_section[..., 1]\n",
    "\n",
    "    giou = iou - np.nan_to_num((enclose_area - union_area)/enclose_area)\n",
    "    return giou\n",
    "# xywh\n",
    "def bbox_iou(boxes1, boxes2):\n",
    "\n",
    "    boxes1_area = boxes1[..., 2] * boxes1[..., 3]\n",
    "    boxes2_area = boxes2[..., 2] * boxes2[..., 3]\n",
    "\n",
    "    boxes1_coor = np.concatenate([boxes1[..., :2] - boxes1[..., 2:] * 0.5,\n",
    "                             boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis=-1)\n",
    "    boxes2_coor = np.concatenate([boxes2[..., :2] - boxes2[..., 2:] * 0.5,\n",
    "                             boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis=-1)\n",
    "\n",
    "    left_up    = np.maximum(boxes1_coor[..., :2], boxes2_coor[..., :2])\n",
    "    right_down = np.minimum(boxes1_coor[..., 2:], boxes2_coor[..., 2:])\n",
    "    \n",
    "    inter_section = np.maximum(right_down - left_up, 0.0)\n",
    "\n",
    "    inter_area = inter_section[..., 0] * inter_section[..., 1]\n",
    "\n",
    "    union_area = boxes1_area + boxes2_area - inter_area\n",
    "    iou       = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps)\n",
    "\n",
    "    return iou\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "def decode_train2(conv_output, output_size, NUM_CLASS, STRIDES, ANCHORS, XYSCALE):\n",
    "#     conv_output = tf.reshape(conv_output,\n",
    "#                              (tf.shape(conv_output)[0], output_size, output_size, 3, 5 + NUM_CLASS))\n",
    "    conv_output = np.reshape(conv_output,\n",
    "                             (conv_output.shape[0], output_size, output_size, 3, 5 + NUM_CLASS))\n",
    "#     conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = tf.split(conv_output, (2, 2, 1, NUM_CLASS),\n",
    "#                                                                           axis=-1)\n",
    "    conv_raw_dxdy, conv_raw_dwdh, conv_raw_conf, conv_raw_prob = np.split(conv_output, [2, 4, 5],\n",
    "                                                                      axis=-1)\n",
    "\n",
    "\n",
    "#     xy_grid = tf.meshgrid(tf.range(output_size), tf.range(output_size))\n",
    "    xy_grid = np.meshgrid(range(output_size), range(output_size))\n",
    "    xy_grid = np.expand_dims(np.stack(xy_grid, axis=-1), axis=2)  # [gx, gy, 1, 2]\n",
    "    xy_grid = np.tile(np.expand_dims(xy_grid, axis=0), [conv_output.shape[0], 1, 1, 3, 1])\n",
    "    xy_grid = xy_grid.astype(np.float32)\n",
    "#     print(xy_grid.dtype, )\n",
    "\n",
    "    pred_xy = ((sigmoid(conv_raw_dxdy) * XYSCALE) - 0.5 * (XYSCALE - 1) + xy_grid) * STRIDES\n",
    "    \n",
    "    pred_wh = (np.exp(conv_raw_dwdh) * ANCHORS)\n",
    "\n",
    "    pred_xywh = np.concatenate([pred_xy, pred_wh], axis=-1)\n",
    "\n",
    "    pred_conf = sigmoid(conv_raw_conf)\n",
    "\n",
    "    pred_prob = sigmoid(conv_raw_prob)\n",
    "\n",
    "\n",
    "    return np.concatenate([pred_xywh, pred_conf, pred_prob], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 4), (1, 13, 13, 3, 4))"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bboxes_pred = decode_train2(pred_large, 13, NUM_CLASS=80, STRIDES=32, ANCHORS=anchors.reshape(3, 3, 2)[2], \n",
    "              XYSCALE=1.)[:, :, :, :, :4]\n",
    "\n",
    "bboxes_true = y_batch[2][:, :, :, :, :4]\n",
    "\n",
    "bboxes_tensor.shape, bboxes_pred.shape\n",
    "# print(bboxes_true, '\\n', '\\n', bboxes_pred)\n",
    "# bbox_iou(bboxes_true, bboxes_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# decode_train2(y_pred, 13, NUM_CLASS=NUM_CLASS, STRIDES=STRIDES, ANCHORS=anchors.reshape(3, 3, 2)[2], \n",
    "#               XYSCALE=1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yolo_loss(y_true, y_pred):\n",
    "    IOU_LOSS_THRESH=0.5\n",
    "    STRIDES = 32\n",
    "    bboxes_tensor = decode_train2(y_pred, 13, NUM_CLASS=80, STRIDES=STRIDES, ANCHORS=anchors.reshape(3, 3, 2)[2], \n",
    "              XYSCALE=1.)\n",
    "\n",
    "    label = y_true\n",
    "    bboxes = bboxes_tensor\n",
    "    grid_size = tf.shape(label)[1]\n",
    "    output_size = grid_size\n",
    "    input_size = STRIDES * output_size\n",
    "\n",
    "    conv_raw_conf = bboxes[..., 4:5]\n",
    "    conv_raw_prob = bboxes[..., 5:]\n",
    "\n",
    "    pred_xywh = bboxes[:, :, :, :, 0:4]  # abs xy wh\n",
    "    pred_conf = bboxes[:, :, :, :, 4:5]\n",
    "\n",
    "    label_xywh = label[:, :, :, :, 0:4]\n",
    "    respond_bbox = label[:, :, :, :, 4:5]\n",
    "    label_prob = label[:, :, :, :, 5:]\n",
    "\n",
    "    giou = tf.expand_dims(bbox_giou(pred_xywh, label_xywh), axis=-1)\n",
    "    input_size = tf.cast(input_size, tf.float64)\n",
    "\n",
    "    bbox_loss_scale = 2.0 - 1.0 * label_xywh[:, :, :, :, 2:3] * label_xywh[:, :, :, :, 3:4] / (input_size ** 2)\n",
    "    giou_loss = tf.cast(respond_bbox, tf.float64) * tf.cast(bbox_loss_scale, tf.float64) * tf.cast((1. - giou), tf.float64)\n",
    "\n",
    "    # iou = utils.bbox_iou(pred_xywh[:, :, :, :, np.newaxis, :], bboxes[:, np.newaxis, np.newaxis, np.newaxis, :, :])\n",
    "    #\n",
    "    # iou = bbox_iou(pred_xywh, bboxes)\n",
    "    iou = bbox_iou(pred_xywh, label_xywh)\n",
    "\n",
    "    # max_iou = tf.expand_dims(tf.reduce_max(iou, axis=-1), axis=-1)\n",
    "    max_iou = tf.reduce_max(iou, axis=-1)[..., tf.newaxis, tf.newaxis]\n",
    "    respond_bgd = tf.cast((1.0 - respond_bbox) * tf.cast(max_iou < IOU_LOSS_THRESH, tf.float64), tf.float64)\n",
    "\n",
    "    conf_focal = tf.pow(respond_bbox - pred_conf, 2)\n",
    "\n",
    "    conf_loss = conf_focal * (\n",
    "            respond_bbox * tf.expand_dims(tf.keras.losses.binary_crossentropy(respond_bbox, pred_conf), axis=-1)  # tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)\n",
    "            +\n",
    "            respond_bgd * tf.expand_dims(tf.keras.losses.binary_crossentropy(respond_bbox, pred_conf), axis=-1)  #  tf.nn.sigmoid_cross_entropy_with_logits(labels=respond_bbox, logits=conv_raw_conf)\n",
    "    )\n",
    "\n",
    "    prob_loss = respond_bbox * tf.expand_dims(tf.keras.losses.categorical_crossentropy(label_prob, tf.nn.softmax(conv_raw_prob)), axis=-1)  #  tf.nn.sigmoid_cross_entropy_with_logits(labels=label_prob, logits=conv_raw_prob)\n",
    "\n",
    "    giou_loss = tf.reduce_mean(tf.reduce_sum(giou_loss, axis=[1, 2, 3, 4]))\n",
    "    conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis=[1, 2, 3, 4]))\n",
    "    prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis=[1, 2, 3, 4]))\n",
    "\n",
    "    return [giou_loss, conf_loss, prob_loss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:93: RuntimeWarning: overflow encountered in exp\n",
      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:47: RuntimeWarning: invalid value encountered in subtract\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Tensor' object has no attribute 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-377-f071e138bbf0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0myolo_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# yolo_loss(y_batch[2], pred_large)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-377-f071e138bbf0>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0myolo_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# yolo_loss(y_batch[2], pred_large)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'numpy'"
     ]
    }
   ],
   "source": [
    "[i.numpy() for i in yolo_loss(y_batch[2], y_batch[2])]\n",
    "# yolo_loss(y_batch[2], pred_large)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# loss = yolo_loss_wrapper(input_shape=(416, 416), \n",
    "#                   STRIDES=[8, 16, 32], \n",
    "#                   NUM_CLASS=3, \n",
    "#                   ANCHORS=anchors.reshape(3, 3, 2), \n",
    "#                   XYSCALES=[1., 1., 1.], \n",
    "#                   IOU_LOSS_THRESH=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = [yolo_loss_wrapper(input_shape=(416, 416), \n",
    "                  STRIDES=[8, 16, 32][i], \n",
    "                  NUM_CLASS=3, \n",
    "                  ANCHORS=anchors.reshape(3, 3, 2)[i], \n",
    "                  XYSCALES=[1., 1., 1.][i], \n",
    "                  IOU_LOSS_THRESH=0.5) for i in range(3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Index out of range using input dim 4; input has only 4 dims for 'loss_1/conv2d_93_loss/strided_slice_8' (op: 'StridedSlice') with input shapes: [?,?,?,?], [5], [5], [5] and with computed input tensors: input[3] = <1 1 1 1 1>.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m   1863\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1864\u001b[0;31m     \u001b[0mc_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_FinishOperation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_desc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1865\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: Index out of range using input dim 4; input has only 4 dims for 'loss_1/conv2d_93_loss/strided_slice_8' (op: 'StridedSlice') with input shapes: [?,?,?,?], [5], [5], [5] and with computed input tensors: input[3] = <1 1 1 1 1>.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-f443340fa1e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m                          loss= {'conv2d_93': losses[0],  # [93, 101, 109]\n\u001b[1;32m      3\u001b[0m                                 \u001b[0;34m'conv2d_101'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlosses\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                                 'conv2d_109': losses[2]})\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mcompile\u001b[0;34m(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m       \u001b[0;31m# Creates the model loss and weighted metrics sub-graphs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compile_weights_loss_and_weighted_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m       \u001b[0;31m# Functions for train, test and predict will\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_compile_weights_loss_and_weighted_metrics\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1708\u001b[0m       \u001b[0;31m#                   loss_weight_2 * output_2_loss_fn(...) +\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1709\u001b[0m       \u001b[0;31m#                   layer losses.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1710\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_total_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1712\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_prepare_skip_target_masks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_prepare_total_loss\u001b[0;34m(self, masks)\u001b[0m\n\u001b[1;32m   1768\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1769\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'reduction'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1770\u001b[0;31m             \u001b[0mper_sample_losses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1771\u001b[0m             weighted_losses = losses_utils.compute_weighted_loss(\n\u001b[1;32m   1772\u001b[0m                 \u001b[0mper_sample_losses\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/losses.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, y_true, y_pred)\u001b[0m\n\u001b[1;32m    213\u001b[0m       \u001b[0mLoss\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0mper\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \"\"\"\n\u001b[0;32m--> 215\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fn_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mget_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-29-d4e1f2abab5a>\u001b[0m in \u001b[0;36myolo_loss\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;31m# pred_conf = pred[:, :, :, :, 4:5]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0mlabel_xywh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0mrespond_bbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0mlabel_prob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36m_slice_helper\u001b[0;34m(tensor, slice_spec, var)\u001b[0m\n\u001b[1;32m    678\u001b[0m         \u001b[0mellipsis_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mellipsis_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0mvar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m         name=name)\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mstrided_slice\u001b[0;34m(input_, begin, end, strides, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask, var, name)\u001b[0m\n\u001b[1;32m    844\u001b[0m       \u001b[0mellipsis_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mellipsis_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m       \u001b[0mnew_axis_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_axis_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m       shrink_axis_mask=shrink_axis_mask)\n\u001b[0m\u001b[1;32m    847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m   \u001b[0mparent_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mstrided_slice\u001b[0;34m(input, begin, end, strides, begin_mask, end_mask, ellipsis_mask, new_axis_mask, shrink_axis_mask, name)\u001b[0m\n\u001b[1;32m   9987\u001b[0m                         \u001b[0mellipsis_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mellipsis_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9988\u001b[0m                         \u001b[0mnew_axis_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_axis_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9989\u001b[0;31m                         shrink_axis_mask=shrink_axis_mask, name=name)\n\u001b[0m\u001b[1;32m   9990\u001b[0m   \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9991\u001b[0m   \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    786\u001b[0m         op = g.create_op(op_type_name, inputs, dtypes=None, name=scope,\n\u001b[1;32m    787\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 788\u001b[0;31m                          op_def=op_def)\n\u001b[0m\u001b[1;32m    789\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0moutput_structure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_def\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_stateful\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m                 \u001b[0;34m'in a future version'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'after %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m                 instructions)\n\u001b[0;32m--> 507\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m     doc = _add_deprecated_arg_notice_to_docstring(\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   3614\u001b[0m           \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3615\u001b[0m           \u001b[0moriginal_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default_original_op\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3616\u001b[0;31m           op_def=op_def)\n\u001b[0m\u001b[1;32m   3617\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_op_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3618\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)\u001b[0m\n\u001b[1;32m   2025\u001b[0m           op_def, inputs, node_def.attr)\n\u001b[1;32m   2026\u001b[0m       self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,\n\u001b[0;32m-> 2027\u001b[0;31m                                 control_input_ops)\n\u001b[0m\u001b[1;32m   2028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2029\u001b[0m     \u001b[0;31m# Initialize self._outputs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m   1865\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1866\u001b[0m     \u001b[0;31m# Convert to ValueError for backwards compatibility.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1867\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mc_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Index out of range using input dim 4; input has only 4 dims for 'loss_1/conv2d_93_loss/strided_slice_8' (op: 'StridedSlice') with input shapes: [?,?,?,?], [5], [5], [5] and with computed input tensors: input[3] = <1 1 1 1 1>."
     ]
    }
   ],
   "source": [
    "model.yolo_model.compile(tf.keras.optimizers.Adam(), \n",
    "                         loss= {'conv2d_93': losses[0],  # [93, 101, 109]\n",
    "                                'conv2d_101': losses[1],\n",
    "                                'conv2d_109': losses[2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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